Phase 2: Object Detection & Segmentation · ~45 minutes · Python
Video Understanding — Temporal Modeling
A video is a sequence of images plus the physics that connects them. Every video model either treats time as an extra axis (3D conv), a sequence to attend over (transformer), or a feature to extract once and pool (2D+pool).
What you will learn
- Distinguish the three main video-modelling approaches (2D+pool, 3D conv, spatio-temporal transformer) and predict their cost and accuracy trade-offs
- Implement frame sampling, temporal pooling, and a 2D+pool baseline classifier in PyTorch
- Explain why I3D's "inflated" 3D kernels transfer well from ImageNet weights and what a factorised (2+1)D conv does differently
- Read the standard action-recognition datasets and metrics: Kinetics-400/600, UCF101, Something-Something V2; top-1 accuracy at the clip and video level
Introduction
- Type: Learn + Build
- Languages: Python
- Prerequisites: Image Classification
- Time: ~45 minutes
Objective
Learning objectives
- Distinguish the three main video-modelling approaches (2D+pool, 3D conv, spatio-temporal transformer) and predict their cost and accuracy trade-offs
- Implement frame sampling, temporal pooling, and a 2D+pool baseline classifier in PyTorch
- Explain why I3D's "inflated" 3D kernels transfer well from ImageNet weights and what a factorised (2+1)D conv does differently
- Read the standard action-recognition datasets and metrics: Kinetics-400/600, UCF101, Something-Something V2; top-1 accuracy at the clip and video level
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